The ability to automatically detect and monitor implanted devices may serve an important role in patient care and
the evaluation of device and treatment efficacy. The purpose of this research was to develop a system for the
automated detection of one-way endobronchial valves implanted as part of a clinical trial for less invasive lung
volume reduction. Volumetric thin section CT data was obtained for 275 subjects; 95 subjects implanted with 246
devices were used for system development and 180 subjects implanted with 354 devices were reserved for testing.
The detection process consisted of pre-processing, pattern-recognition based detection, and a final device selection.
Following the pre-processing, a set of classifiers were trained using AdaBoost to discriminate true devices from
false positives (such as calcium deposits). The classifiers in the cascade used simple features (the mean or max
attenuation) computed near control points relative to a template model of the valve. Visual confirmation of the
system output served as the gold standard. FROC analysis was performed for the evaluation; the system could be set
so the mean sensitivity was 96.5% with a mean of 0.18 false positives per subject. These generic device modeling
and detection techniques may be applicable to other devices and useful for monitoring the placement and function of
implanted devices.